The Division of Agricultural Economics & Statistics at the Faculty of Agriculture (FoA), SKUAST-K, inaugurated two one-week skill development training programmes on Tuesday at Government Degree College (GDC), Sopore. The programmes are titled “Applied Statistics and Machine Learning in Agriculture and Allied Sciences” and “Statistical Software Ventures in Data Analysis”, and are sponsored under the Micro, Small, and Medium Enterprises (MSME) scheme.
The aim of these training programmes is to equip participants with skills in machine learning tools and their applications across various employment-generating fields, the SKUAST-K said in a press release issued here.
Trainees will gain insights into applied statistics and machine learning in agriculture and allied sciences, positioning them for careers in data science, machine learning engineering and AI. The growing demand for professionals in these areas, including roles focused on AI ethics and data privacy, underscores the necessity of these skills.
Prof Showkat Maqbool, Coordinator of the training programmes, emphasised the significance of these initiatives in fostering essential skills among participants.
Dean of FoA, Prof (Dr) Rehana Habib Kant, highlighted the importance of the training for the youth, commending the university’s efforts to provide crucial training and promote entrepreneurship in agriculture and allied activities.
Principal of GDC Sopore, Prof Bashir Ahmad, expressed gratitude to the Dean and Coordinator for selecting the college to host these skill training programmes. He underscored the importance of skill development for student employment generation.
Prof Nisar Ahmad, Co-Coordinator of the programmes, provided a detailed report on the training sessions and the specialists who will deliver lectures throughout the week. He thanked SKUAST-K authorities, especially the Dean and Coordinator, for facilitating these essential training opportunities at GDC Sopore.
The inaugural function concluded with a formal vote of thanks presented by Dr Fahim Jeelani Wani.
